Paper Info
Title
Utilizing Subject-Specific Discriminative EEG Features for Classification of Motor Imagery Directions
Abstract
Electroencephalogram (EEG)-based BrainComputer Interface (BCI) technology needs efficient algorithms to find distinct EEG patterns/features to realize applications with distinct high-dimensional control signals. This paper proposes a novel feature extraction methodology for separating EEG patterns associated right hand motor imagery performed towards left and right directions. The most discriminative subject-specific feature set is chosen based on Fisher's ratio of absolute phase values of EEG in 6 low frequency sub bands. Using this, the proposed BCI system is capable of providing better classification results than state-ofthe-art methodology with fixed channels, fusing absolute phase and spatial features from selected subject-specific discriminative channels. Experimental analysis shows that though parietal lobe is vital in providing distinguishable features, the channel set that provide maximum accuracy, is highly subject-specific. Hence, subject-specific BCI that can decode finer parameters of imagined movement are feasible and further research to understand the activations elicited in parietal lobe can contribute towards robust BCI systems.
Year
DOI
Venue
2019
10.1109/ICAwST.2019.8923216
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)
Keywords
Field
DocType
Electroencephalogram (EEG),absolute phase,motor imagery kinematics,Fisher’s ratio,channel selection
Absolute phase,Pattern recognition,Computer science,Subject specific,Brain–computer interface,Feature extraction,Artificial intelligence,Discriminative model,Electroencephalography,Motor imagery,Parietal lobe
Conference
ISSN
ISBN
Citations 
2325-5986
978-1-7281-3822-0
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Kavitha P. Thomas1707.68
Neethu Robinson2185.09
A. Prasad Vinod332850.06